Distributed Simulation of Statevectors and Density Matrices
- URL: http://arxiv.org/abs/2311.01512v1
- Date: Thu, 2 Nov 2023 18:00:36 GMT
- Title: Distributed Simulation of Statevectors and Density Matrices
- Authors: Tyson Jones, B\'alint Koczor, Simon C. Benjamin
- Abstract summary: This manuscript presents a plethora of novel algorithms for distributed full-state simulation of gates, operators, noise channels and other calculations in digital quantum computers.
We show how a simple, common but seemingly restrictive distribution model actually permits a rich set of advanced facilities.
Our results are derived in language familiar to a quantum information theory audience, and our algorithms formalised for the scientific simulation community.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Classical simulation of quantum computers is an irreplaceable step in the
design of quantum algorithms. Exponential simulation costs demand the use of
high-performance computing techniques, and in particular distribution, whereby
the quantum state description is partitioned between a network of cooperating
computers - necessary for the exact simulation of more than approximately 30
qubits. Distributed computing is notoriously difficult, requiring bespoke
algorithms dissimilar to their serial counterparts with different resource
considerations, and which appear to restrict the utilities of a quantum
simulator. This manuscript presents a plethora of novel algorithms for
distributed full-state simulation of gates, operators, noise channels and other
calculations in digital quantum computers. We show how a simple, common but
seemingly restrictive distribution model actually permits a rich set of
advanced facilities including Pauli gadgets, many-controlled many-target
general unitaries, density matrices, general decoherence channels, and partial
traces. These algorithms include asymptotically, polynomially improved
simulations of exotic gates, and thorough motivations for high-performance
computing techniques which will be useful for even non-distributed simulators.
Our results are derived in language familiar to a quantum information theory
audience, and our algorithms formalised for the scientific simulation
community. We have implemented all algorithms herein presented into an
isolated, minimalist C++ project, hosted open-source on Github with a
permissive MIT license, and extensive testing. This manuscript aims both to
significantly improve the high-performance quantum simulation tools available,
and offer a thorough introduction to, and derivation of, full-state simulation
techniques.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Compact quantum algorithms for time-dependent differential equations [0.0]
We build on an idea based on linear combination of unitaries to simulate non-unitary, non-Hermitian quantum systems.
We generate hybrid quantum-classical algorithms that efficiently perform iterative matrix-vector multiplication and matrix inversion operations.
arXiv Detail & Related papers (2024-05-16T02:14:58Z) - TANQ-Sim: Tensorcore Accelerated Noisy Quantum System Simulation via QIR on Perlmutter HPC [16.27167995786167]
TANQ-Sim is a full-scale density matrix based simulator designed to simulate practical deep circuits with both coherent and non-coherent noise.
To address the significant computational cost associated with such simulations, we propose a new density-matrix simulation approach.
To optimize performance, we also propose specific gate fusion techniques for density matrix simulation.
arXiv Detail & Related papers (2024-04-19T21:16:29Z) - Simulator Demonstration of Large Scale Variational Quantum Algorithm on HPC Cluster [0.0]
This study aims to accelerate quantum simulation using two newly proposed methods.
We achieved 200 times higher speed over VQE simulations and demonstrated 32 qubits ground-state energy calculations in acceptable time.
arXiv Detail & Related papers (2024-02-19T06:34:01Z) - A Herculean task: Classical simulation of quantum computers [4.12322586444862]
This work reviews the state-of-the-art numerical simulation methods that emulate quantum computer evolution under specific operations.
We focus on the mainstream state-vector and tensor-network paradigms while briefly mentioning alternative methods.
arXiv Detail & Related papers (2023-02-17T13:59:53Z) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Parallel Simulation of Quantum Networks with Distributed Quantum State
Management [56.24769206561207]
We identify requirements for parallel simulation of quantum networks and develop the first parallel discrete event quantum network simulator.
Our contributions include the design and development of a quantum state manager that maintains shared quantum information distributed across multiple processes.
We release the parallel SeQUeNCe simulator as an open-source tool alongside the existing sequential version.
arXiv Detail & Related papers (2021-11-06T16:51:17Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
We present a constructive algorithm for generating quantum circuits with time-independent depth.
We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model.
In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
arXiv Detail & Related papers (2021-04-01T19:06:00Z) - Realistic simulation of quantum computation using unitary and
measurement channels [1.406995367117218]
We introduce a new simulation approach that relies on approximating the density matrix evolution by a sum of unitary and measurement channels.
This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known approaches.
arXiv Detail & Related papers (2020-05-13T14:29:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.